Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data

Qi Li and Jeffrey Racine

Journal of Business & Economic Statistics, 2008, vol. 26, 423-434

Abstract: We propose a new nonparametric conditional cumulative distribution function kernel estimator that admits a mix of discrete and categorical data along with an associated nonparametric conditional quantile estimator. Bandwidth selection for kernel quantile regression remains an open topic of research. We employ a conditional probability density function-based bandwidth selector proposed by Hall, Racine, and Li that can automatically remove irrelevant variables and has impressive performance in this setting. We provide theoretical underpinnings including rates of convergence and limiting distributions. Simulations demonstrate that this approach performs quite well relative to its peers; two illustrative examples serve to underscore its value in applied settings.

Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (112)

Downloads: (external link)
http://pubs.amstat.org/doi/abs/10.1198/073500107000000250 full text (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:26:y:2008:p:423-434

Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano

More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().